Observation of Colloidal Gold Labelled Platelet Surface Receptors and the Underlying Cytoskeleton Using High Voltage Electron Microscopy and Scanning Electron Microscopy

Fibrinogen conjugated to colloidal gold or colloidal gold-monoclonal anti-glycoprotein IIb/IIIa (fibrinogen receptor) was used to label the receptor on platelets. Whole mount preparations were examined by stereo pair high voltage electron microscopy and then by scanning electron microscopy to determine the feasibility of this approach in detecting the number of receptors and their location relative to the cytoskeletal and surface structure. Both the ligand-gold and antibody-gold labels were effective. The relative numbers of receptors could be seen and their relationship to cytoskeletal structure could be determined. Marked differences in receptor number and distribution were observed when platelets in different stages of activation were compared. In co-cultured macrophages and platelets, receptors were found exclusively on platelets or on pieces of platelet membrane adherent to macrophages

Making Decisions that Reduce Discriminatory Impacts

As machine learning algorithms move into realworld settings, it is crucial to ensure they are aligned with societal values. There has been much work on one aspect of this, namely the discriminatory prediction problem: How can we reduce discrimination in the predictions themselves? While an important question, solutions to this problem only apply in a restricted setting, as we have full control over the predictions. Often we care about the non-discrimination of quantities we do not have full control over. Thus, we describe another key aspect of this challenge, the discriminatory impact problem: How can we reduce discrimination arising from the real-world impact of decisions? To address this, we describe causal methods that model the relevant parts of the real-world system in which the decisions are made. Unlike previous approaches, these models not only allow us to map the causal pathway of a single decision, but also to model the effect of interference–how the impact on an individual depends on decisions made about other people. Often, the goal of decision policies is to maximize a beneficial impact overall. To reduce the discrimination of these benefits, we devise a constraint inspired by recent work in counterfactual fairness (Kusner et al., 2017), and give an efficient procedure to solve the constrained optimization problem. We demonstrate our approach with an example: how to increase students taking college entrance exams in New York City public schools

On Quartet Superfluidity of Fermionic Atomic Gas

Possibility of a quartet superfluidity in fermionic systems is studied as a new aspect of atomic gas at ultra low temperatures. The four-fold degeneracy of hyperfine state and moderate coupling is indispensable for the quartet superfluidity to occur. Possible superconductivity with quartet condensation in electron systems is discussed.Comment: 7 pages, 1 figure. J. Phys. Soc. Jpn. vol.74 (2005) No.7, in press; Note added for related previous works; some typographic errors revise

Biosensors for the monitoring of harmful algal blooms

Peer Reviewed Paper. DOI: https://doi.org/10.1016/j.copbio.2017.02.018 Citation: McPartlin, D. A., Loftus, J. H., Crawley, A. S., Silke, J., Murphy, C. S., & O’Kennedy, R. J. (2017). Biosensors for the monitoring of harmful algal blooms. Current Opinion in Biotechnology, 45, 164–169. https://doi.org/10.1016/j.copbio.2017.02.018Harmful algal blooms (HABs) are a major global concern due to their propensity to cause environmental damage, healthcare issues and economic losses. In particular, the presence of toxic phytoplankton is a cause for concern. Current HAB monitoring programs often involve laborious laboratory-based analysis at a high cost and with long turnaround times. The latter also hampers the potential to develop accurate and reliable models that can predict HAB occurrence. However, a promising solution for this issue may be in the form of remotely deployed biosensors, which can rapidly and continuously measure algal and toxin levels at the point-of-need (PON), at a low cost. This review summarises the issues HABs present, how they are difficult to monitor and recently developed biosensors that may improve HAB-monitoring challenges

Electrochemical Characterization of Self-Assembled Monolayers on Gold Substrates Derived from Thermal Decomposition of Monolayer-Protected Cluster Films

Networked films of monolayer-protected clusters (MPCs), alkanethiolate-stabilized gold nanoparticles, can be thermally decomposed to form stable gold on glass substrates that are subsequently modified with self-assembled monolayers (SAMs) for use as modified electrodes. Electrochemical assessment of these SAM-modified gold substrates, including double-layer capacitance measurements, linear sweep desorption of the alkanethiolates, and diffusional redox probing, all show that SAMs formed on gold supports formed from thermolysis of MPC films possess substantially higher defect density compared to SAMs formed on traditional evaporated gold. The density of defects in the SAMs on thermolyzed gold is directly related to the strategies used to assemble the MPC film prior to thermolysis. Specifically, gold substrates formed from thermally decomposing MPC films formed with electrostatic bridges between carboxylic acid-modified MPCs and metal ion linkers are particularly sensitive to the degree of metal exposure during the assembly process. While specific metal dependence was observed, metal concentration within the MPC precursor film was determined to be a more significant factor. Specific MPC film linking strategies and pretreatment methods that emphasized lower metal exposure resulted in gold films that supported SAMs of lower defect density. The defect density of a SAM-modified electrode is shown to be critical in certain electrochemical experiments such as protein monolayer electrochemistry of adsorbed cytochrome c. While the thermal decomposition of nanoparticle film assemblies remains a viable and interesting technique for coating both flat and irregular shaped substrates, this study provides electrochemical assessment tools and tactics for determining and controlling SAM defect density on this type of gold structure, a property critical to their effective use in subsequent electrochemical applications

Counterfactual Fairness

Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In many of these scenarios, previous decisions have been made that are unfairly biased against certain subpopulations, for example those of a particular race, gender, or sexual orientation. Since this past data may be biased, machine learning predictors must account for this to avoid perpetuating or creating discriminatory practices. In this paper, we develop a framework for modeling fairness using tools from causal inference. Our definition of counterfactual fairness captures the intuition that a decision is fair towards an individual if it the same in (a) the actual world and (b) a counterfactual world where the individual belonged to a different demographic group. We demonstrate our framework on a real-world problem of fair prediction of success in law school